In this chapter, you were introduced to machine learning and the two main types of supervised machine learning tasks – regression and classification. Then, you were presented with the potential benefits of feature selection on the performance of the models carrying out these tasks. At the heart of this chapter were two demonstrations of how genetic algorithms can be utilized to enhance the performance of such models via feature selection. In the first case, we pinpointed the genuine features that were generated by the Friedman-1 Test regression problem, while, in the other case, we selected the most beneficial features of the Zoo classification dataset.
In the next chapter, we will look at another possible way of enhancing the performance of supervised machine learning models, namely hyperparameter tuning.
